AI ML Jobs

The artificial intelligence, machine learning, and deep learning jobs

Senior Data Scientist, Ranking & Recommendations at Traveloka (Singapore)

As a Senior Data Scientist for Ranking and Recommendations you will apply machine learning techniques such as collaborative filtering, reinforcement learning, and learning-to-rank to solve the quests of relevance prediction.

On a typical day, you will:

  • build systems that delight millions of travellers,
  • be a guide and mentor to your junior colleagues,
  • work with the Data Analytics team to analyse exciting behavioral data and find new high-impact opportunities,
  • build and tune machine learning models to improve recommendations and rankings,
  • use Traveloka’s experimentation platform to track and measure your models’ success,
  • effectively communicate your projects to your stakeholders and higher management, and
  • be a valued voice in our effort to constantly improve our practices and frameworks.

You will own data products such as:

  • Hotel recommendations using matrix-factorisation, SVD, or ALS
  • Car rental search ranking using AdaRank, SVM-NDCG, or multinomial logistic regression
  • Restaurant and dish recommendation using content-based filtering or pattern mining techniques such as Apriori or association rules

Working in Traveloka:

  • You will work in cross-functional teams and meet great people regularly from top tier technology, consulting, product, or academic background.
  • We work in an open environment where there are no boundaries or power distance.
  • Everyone is encouraged to speak their mind, propose ideas, influence others, and continuously grow themselves.
  • Get the exposure to multi-aspect, collaborative, intensive startup experience with our recent expansion into Southeast Asia and exploration of new products.

Required Academic Qualifications

  • Masters/PhD degree from a top university in a quantitative field (Computer Science, Engineering, Physics, Mathematics or similar), or equivalent experience
  • Very good theoretical understanding of relevant statistical models, their inner workings, assumptions, and limitations
  • Very good understanding of evaluation metrics for relevance ranking, such as N/DCG, MAP, expected reciprocal rank, and classical MAE/MSE/RMSE-based metrics
  • Academic ability to conceptualise higher-order success metrics such as serendipity and exposure diversity

Required Hands-On Experience

  • 4+ years of industry experience in building recommender/ranking systems using either collaborative or content-based techniques for a consumer-facing company
  • 3+ years stakeholder management skills and the ability to manage timelines and expectations
  • Strong hands-on experience in the ML life-cycle for data scientists (training, testing, tuning, and performance monitoring), and a good understanding of how your friendly Data Engineering and Data Ops colleagues deploy your models to production.
  • Experience with shuffling around data in cloud environments (preferably GCP: BigQuery, Pub/Sub, Dataproc) and performing the data munging required for finding new opportunities in our data.